In recent years, indoor environment reconstruction has emerged as a significant and challenging task to provide a semantically-rich and geometrically-accurate indoor models. Indoor models are regarded as fundamental infrastructure for many applications such as navigation guidance, emergency management, building maintenance and renovation planning, and a range of indoor location-based services (e.g. way-finding, contextualized content delivery, etc.).

To date, a variety of sensors, e.g., cameras (monocular, stereo, video or panoramic), laser scanners and depth cameras are employed to collect the indoor scene data. Therefore, an increasing number of datasets dedicated to various applications have become available. These are of great importance to benchmark the state-of-the-art. However, unlike with outdoor environments, indoor environment reconstruction still poses specific challenges due to complicated indoor structure layouts, complex interactions between objects, clutter and occlusions. Moreover, because semantic labeling (e.g. object types) plays an important role in a semantically-rich indoor model, robust semantic labeling from various sensor data also remains a big challenge. Therefore, the processing pipeline from raw data to indoor geometric/semantic models has become a hot topic in the photogrammetry and the computer vision communities. Over the past several years, a biannual ISPRS workshop series entitled Indoor 3D had been successfully held. It has focused on research and development in the area of indoor spatial information, aiming to promote international state-of-the-art research. The workshop has proven as one of the hottest scientific events in the ISPRS community. The purpose of this theme issue is to increase interdisciplinary interaction and collaboration in indoor environment modeling among researchers working in photogrammetry, computer vision, and robotics (for mapping and modelling), which has been actively promoted by ISPRS WG IV/5, I/6, I/7 and ICWG I/IV.

This theme issue covers a range of topics on geometric, semantic and topologic modeling of indoor environments from the raw data acquired by LiDAR, depth cameras and passive image sensors, in terms of spatial data standards (such as Industry Foundation Classes (IFC), City Geography Markup Language (CityGML) LoD4, and Indoor Geography Markup Language (IndoorGML), etc.). Concerning the geometric modeling, polygonal structured models, room layout estimation and indoor-outdoor seamless modeling are particularly relevant to this theme issue. The reconstruction of detailed 3D indoor models with interior (static/dynamic) objects is also of relevance. Semantic modeling mainly focuses on semantic labeling and parsing of the indoor environment. The semantically-rich representation of indoor environment requires not only geometry and semantics, but also topology such as connectivity between rooms, containment between doors and walls, etc. Therefore, the reconstruction of the topologic model of the indoor environment is relevant to this theme issue as well. The list of suggested topics includes, but is not limited to, the following:

Indoor scene understanding by combining both spatial and temporal consistency

The theme issue seeks high-quality research submissions in all aspects of indoor environment modeling. Test on benchmark datasets (e.g. ISPRS benchmark datasets on indoor modeling, and multisensorial indoor mapping and positioning, etc.) are strongly encouraged. Papers must be original contributions, not previously published or submitted to other journals. Submissions based on previous published or submitted conference papers may be considered provided they are considerably improved and extended. Papers must follow the instructions for authors at http://www.elsevier.com/journals/isprs-journal-of-photogrammetry-and-remote-sensing/0924-2716/guide-for-authors